Robust unsupervised detection of action potentials with probabilistic models
Robust unsupervised detection of action potentials with probabilistic models
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DOI：
10.1109/tbme.2007.912433
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发表时间：
2008-04-01
影响因子：
4.6
通讯作者：
Nenadic, Zoran
中科院分区：
文献类型：
--
作者：
Benitez, Raul;Nenadic, Zoran
We develop a robust and fully unsupervised algorithm for the detection of action potentials from extracellularly recorded data. Using the continuous wavelet transform allied to probabilistic mixture models and Bayesian probability theory, the detection of action potentials is posed as a model selection problem. Our technique provides a robust performance over a wide range of simulated conditions, and compares favorably to selected supervised and unsupervised detection techniques.
